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15 results on '"Li, Qiangzi"'

2. ATP-free in vitro biotransformation of starch-derived maltodextrin into poly-3-hydroxybutyrate via acetyl-CoA

Author

Wei, Xinlei, primary, Yang, Xue, additional, Hu, Congcong, additional, Li, Qiangzi, additional, Liu, Qianqian, additional, Wu, Yue, additional, Xie, Leipeng, additional, Ning, Xiao, additional, Li, Fei, additional, Cai, Tao, additional, Zhu, Zhiguang, additional, Zhang, Yi-Heng P. Job, additional, Zhang, Yanfei, additional, Chen, Xuejun, additional, and You, Chun, additional

Published
2024
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8. Monitoring of Cotton Boll Opening Rate Based on UAV Multispectral Data.

Author

Wang, Yukun, Xiao, Chenyu, Wang, Yao, Li, Kexin, Yu, Keke, Geng, Jijia, Li, Qiangzi, Yang, Jiutao, Zhang, Jie, Zhang, Mingcai, Lu, Huaiyu, Du, Xin, Du, Mingwei, Tian, Xiaoli, and Li, Zhaohu

Subjects
NORMALIZED difference vegetation index, DRONE aircraft, LANDSAT satellites, NONDESTRUCTIVE testing, RANDOM forest algorithms, PRECISION farming, CROP growth
Abstract

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture. [ABSTRACT FROM AUTHOR]

Published
2024
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